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The research presented in this article is aimed at developing an automated imaging system for classification of tissues in medical images obtained from Computed Tomography (CT) scans. The article focuses on using multi-resolution texture analysis, specifically: the Haar wavelet, Daubechies wavelet, Coiflet wavelet, and the ridgelet. The algorithm consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The classification step is implemented using a cross-validation Classification and Regression Tree approach. A comparison of wavelet-based and ridgelet-based algorithms is presented. Tests on a large set of chest and abdomen CT images indicate that, among the three wavelet-based algorithms, the one using texture features derived from the Haar wavelet transform clearly outperforms the one based on Daubechies and Coiflet transform. The tests also show that the ridgelet-based algorithm is significantly more effective and that texture features based on the ridgelet transform are better suited for texture classification in CT medical images.

As part of the assessment process of our BS in Information Technology (IT), we sought the perspective of IT employers on needed skills and knowledge for a career in IT. To this end, we conducted structured interviews with 10 IT employers in the Chicago area.Starting with an open-ended query, we asked for knowledge, skills or competencies that they particularly value when hiring IT graduates. We then asked for feedback on four preselected competencies. All four competencies were framed in technology-independent terms and are consistent with the ACM 2005 IT curricula guidelines. Two of the competencies addressed abstraction and modeling, which directly correspond to learning outcomes in the core IT Fundamentals in the ACM guidelines. A third competency addressed object-based user interface development. A final competency focused on the distinction between interface and implementation. For each competency, we asked about their value with respect to their IT positions and how they assess the competency in a job candidate.One major finding from the interviews is that knowledge specific to a particular language or platform was rarely cited as relevant and often explicitly noted as irrelevant. The competency on abstraction was uniformly rated as needed, often receiving critical ratings. Modeling and ability to distinguish between interface and implementation were also indicated as needed by nearly every interviewee. Object-based user interface development was considered useful knowledge but only needed for specific types of positions. For assessing competencies, a common approach involved asking prospective recruits to present an experience where they applied principles associated with a particular competency. Several interviewees also assess level of understanding by presenting candidates with hypothetical scenarios and observing their problem-solving process. To complement the detailed commentary provided by our interviews, we plan a survey with a large number of respondents for more reliable estimates of how employers value and assess IT competencies.